U.S. patent application number 13/898092 was filed with the patent office on 2013-09-26 for noninvasive diagnostic system.
This patent application is currently assigned to JointVue, LLC. The applicant listed for this patent is JointVue, LLC. Invention is credited to Richard D. Komistek, Mohamed R. Mahfouz, Ray C. Wasielewski.
Application Number | 20130253379 13/898092 |
Document ID | / |
Family ID | 42396088 |
Filed Date | 2013-09-26 |
United States Patent
Application |
20130253379 |
Kind Code |
A1 |
Mahfouz; Mohamed R. ; et
al. |
September 26, 2013 |
NONINVASIVE DIAGNOSTIC SYSTEM
Abstract
A method for diagnosing a joint condition includes in one
embodiment: creating a 3d model of the patient specific bone;
registering the patient's bone with the bone model; tracking the
motion of the patient specific bone through a range of motion;
selecting a database including empirical mathematical descriptions
of the motion of a plurality actual bones through ranges of motion;
and comparing the motion of the patient specific bone to the
database.
Inventors: |
Mahfouz; Mohamed R.;
(Knoxville, TN) ; Wasielewski; Ray C.; (New
Albany, OH) ; Komistek; Richard D.; (Knoxville,
TN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
JointVue, LLC |
Columbus |
OH |
US |
|
|
Assignee: |
JointVue, LLC
Columbus
OH
|
Family ID: |
42396088 |
Appl. No.: |
13/898092 |
Filed: |
May 20, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
12364267 |
Feb 2, 2009 |
8444564 |
|
|
13898092 |
|
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|
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Current U.S.
Class: |
600/595 |
Current CPC
Class: |
A61B 5/0002 20130101;
A61B 5/1121 20130101; G16H 40/63 20180101; A61B 5/7267 20130101;
A61B 8/0875 20130101; G16H 20/30 20180101; A61B 8/4236 20130101;
A61B 2034/105 20160201; A61B 2562/0219 20130101; G16H 50/50
20180101; A61B 5/002 20130101; A61B 8/466 20130101; A61B 2034/2051
20160201; A61B 5/0059 20130101; A61B 5/112 20130101; A61B 34/20
20160201; A61B 8/4263 20130101; A61B 8/4477 20130101; A61B 8/5223
20130101; A61B 2562/02 20130101; A61B 5/1036 20130101; A61B
2034/2048 20160201; A61B 5/72 20130101; A61B 5/11 20130101; A61B
5/6831 20130101; A61B 5/743 20130101; A61B 8/4254 20130101; A61B
5/4585 20130101; A61B 8/5238 20130101; A61B 5/0004 20130101; A61B
8/0858 20130101; A61B 2562/0247 20130101; A61B 5/4528 20130101;
A61B 5/7435 20130101; A61B 8/483 20130101; A61B 5/1127 20130101;
A61B 5/4533 20130101; A61B 5/6807 20130101; A61B 5/04 20130101;
A61B 5/1038 20130101; A61B 5/0488 20130101; A61B 5/1114 20130101;
A61B 5/6833 20130101; A61B 8/467 20130101; G06N 3/084 20130101;
A61B 8/4472 20130101; A61B 2562/0204 20130101; A61B 5/6828
20130101; A61B 2562/046 20130101; A61B 8/4227 20130101 |
Class at
Publication: |
600/595 |
International
Class: |
A61B 5/04 20060101
A61B005/04 |
Claims
1. A method of analyzing motion of a patient's bone, the method
comprising: creating a 3D model of the patient's bone; registering
the patient's bone with the bone model; tracking the motion of the
patient's bone through a range of motion; selecting a database
comprising empirical mathematical descriptions of the motion of a
plurality of actual bones through a range of motion; and comparing
the motion of the patient's bone to the database.
2. The method of claim 1 further comprising: attaching one or more
inertia based sensors near a patient's joint; gathering vibrational
data as the patient joint is moved through the range of motion;
comparing the vibrational data to an empirical database of
vibration data for the relative motion of one or more joints for
diagnostic purposes, wherein the motion of the patient's bone is
tracked with the inertia based sensors.
3. The method of claim 2 wherein each of the one or more inertia
based sensors includes an ultrasound transducer and an inertial
sensor.
4. The method of claim 2 wherein attaching the one or more inertia
based sensors near the patient's joint includes releasably coupling
a bone tracking brace proximate the joint, the bone tracking brace
including a ultrasound transducer and a inertial sensor.
5. The method of claim 2 further comprising: wirelessly
transmitting data obtained by the inertial based sensor while
moving the joint through the range of motion.
6. A method of monitoring a joint, the method comprising: placing
an inertial sensor approximate the joint; gathering vibration data
using the inertial sensor while moving the joint through a range of
motion; and simultaneously with gathering the vibration data,
gathering kinematics data pertaining to the joint.
7. The method of claim 6, wherein the vibration data is
time-synchronized with the kinematics data.
8. The method of claim 6 further comprising: analyzing the
vibration data using a neural network.
9. The method of claim 8 further comprising: training the neural
network.
10. The method of claim 8, wherein analyzing the vibration data
using the neural network includes diagnosing at least one of a
presence and a severity of a joint condition.
11. The method of claim 8, wherein analyzing the vibration data
using the neural network includes transmitting the vibration data
across a network and receiving data pertaining to the joint
condition via the network.
12. A method of diagnosing a joint condition, the method
comprising: creating a model of a joint; obtaining vibration data
and kinematics data while moving the joint through a range of
motion; correlating the kinematics data with the model of the joint
to generate a model of the joint's motion; and analyzing the
vibration data and the model of the joint's motion to diagnose at
least one of the presence and severity of a joint condition.
13. The method of claim 12 wherein creating the model of the joint
includes creating three dimensional models of a plurality of bones
of the joint.
14. The method of claim 12 wherein creating the three dimensional
models of the plurality of bones of the joint includes conforming
at least one model bone to an actual patient bone using measured
data.
15. The method of claim 14 wherein the measured data is ultrasound
imaging data.
16. The method of claim 12 wherein obtaining vibration data and
kinematics data includes obtaining time-synchronized vibration and
kinematics data.
17. The method of claim 16 wherein obtaining vibration data and
kinematics data includes attaching a frame proximate the joint,
wherein the frame includes at least one inertial sensor and at
least one ultrasound transducer.
18. The method of claim 12 wherein analyzing the vibration data is
performed using a neural network.
19. The method of claim 18 further comprising training the neural
network.
Description
[0001] This Application is a continuation of U.S. patent
application Ser. No. 12/364,267, filed Feb. 2, 2009, the disclosure
of which is incorporated by reference herein in its entirety.
FIELD OF THE INVENTION
[0002] The present invention relates to diagnosis of bodily
abnormalities, and more particularly, to devices and methods for
evaluating the physiological condition of bodily tissue to discern
whether abnormalities exist and the extent of any abnormalities.
While the exemplary embodiments disclosed herein are utilized and
discussed with respect to a human knee joint, it is to be
understood that other joints and bodily tissues may be likewise
diagnosed.
BACKGROUND OF THE INVENTION
[0003] In humans, the knee joint is functionally controlled by a
mechanical system governed by three unique types of forces: (1)
active forces resulting in motion, such as those resulting from
muscle flexing or relaxing; (2) constraining forces that constrain
motion, such as those resulting from ligaments being in tension;
and (3) compressive forces that resist motion, such as those acting
upon bones. In addition to the foregoing bodily tissues accounting
for these three forces, cartilage and meniscus also produce a
dampening effect dissipating the compressive forces propagating to
other joints.
[0004] Knee joint motions are stabilized primarily by four
ligaments, which restrict and regulate the relative motion between
the femur, tibia, and patella. These ligaments are the anterior
cruciate ligament (ACL), the posterior cruciate ligament (PCL), the
medial collateral ligament (MCL), and the lateral collateral
ligament (LCL), as shown in FIGS. 1 and 2. An injury to any of
these ligaments or other soft-tissue structures can cause
detectable changes in knee kinematics and the creation of
detectable patterns of vibration representative of the type of knee
joint injury and the severity of the injury. These visual and
auditory changes are produced by the bones while moving in a
distorted kinematic pattern, and they differ significantly from the
look and vibration of a properly balanced knee joint moving through
a range of motion.
[0005] Many research studies have been conducted to assess knee
vibration and correlate it with clinical data regarding various
joint problems using microphones with or without stethoscope
equipment. However, it has been shown that microphones cannot
reliably detect joint frequencies, especially those experiencing
strong interference from noise, and the signal clearance can
substantially influenced by skin friction. It has been hypothesized
that the failure associated with the interpretation of sound
emissions and possible reasons for occurrence is directly
attributable to the complicity of the sound signal, the unknown
noise factors, and unknown sound center. It is desirable,
therefore, to provide a diagnostic tool that compares patient
specific data with kinematic data by providing visual feedback to
clinicians.
SUMMARY OF THE INVENTION
[0006] The present invention, in one embodiment, provides a patient
specific 3D model of a patient's joint, including bone and soft
tissue. This model is then registered to the patient's actual bone
so that as the joint is taken through a range of motion it can be
visualized on a computer screen. A physician can then use the
computer generated image to make a diagnosis or compare the motion
of the actual bone to a database of clinically relevant information
on desirable or undesirable joint motion.
[0007] The exemplary embodiments of the present invention include a
diagnostic system for mammalian bodies to determine the type of
injury and extent of injury using kinematic data and/or vibration
data. In particular, an exemplary method and embodiment are
directed to a knee joint diagnostic system for automatically
determining the type of injury and the extent to which ligaments,
muscles, bones, meniscus, and cartilage may be affected by an
injury through analyzing the kinematics of the knee joint, while
also analyzing the pattern and special distribution of the
vibration produced knee joint movement. An exemplary process flow
diagram for this exemplary method is shown in FIG. 3.
[0008] To evaluate knee kinematics, patient-specific 3D models of
the distal femur, proximal tibia, and the patella are constructed
using pulse echo A-mode ultrasound based 3D model reconstruction
technology. In addition, patient-specific kinematic data is
obtained for the motions of the femur, tibia, and patella using
pulse A-mode ultrasound. Finally, patient specific vibration data
is obtained while the knee joint is taken through a range of motion
and loaded in real-world conditions. In exemplary form, the
vibration data and kinematic data are taken at the same time using
the single data acquisition device. In a further exemplary
embodiment, if the data is acquired in a physician's office, the
data is displayed in real-time on a split screen monitor. If,
however, the data is acquired outside of the doctor's office, a
recording device and memory may be utilized to record the data in a
time synched manner. In a yet a further exemplary embodiment, the
patient may be given an actuator that is operative to note the
general time frame within which the patient felt a particular pain
or severe pain to allow a correlation between pain felt by the
patient and the kinematics and vibration occurring at roughly the
same time.
[0009] Patient-specific data is analyzed by a trained neural
network in order to provide an automated output as to the existence
of an injury, the type of injury, and the severity of the injury.
This neural network may be accessible via the internet or may
reside on a physician's local computer. In addition, or in the
alternative, patient-specific data may be analyzed by a physician
to make the diagnosis directly without the aid of the neural
network.
[0010] Using the exemplary methods and devices as disclosed herein,
a physician may diagnose a bodily injury without requiring
experimental surgery or requiring exposure of the patient to
radiation from still X-rays or fluoroscopy. In addition, the data
taken regarding each patient is continuous through a range of
motion, in contrast to X-rays and fluoroscopy which take images at
distinct points with significant range of motion gaps. In addition,
data taken in accordance with the exemplary method and devices
disclosed herein also contrasts data taken by a magnetic resonance
imaging machine, not only because the data taken is continuous
along the range of motion, but also because the bodily portion
evaluated is acting under loaded conditions in a dynamic
environment.
[0011] It is an object of the present invention to provide a method
of creating a three dimensional model of a patient's bone using
tracked pulse-echo A-Mode ultrasound and atlas-based deformable
models.
[0012] It is another object of the present invention to provide a
method of registering a patient's bone with a three dimensional
model of the patient's actual bone.
[0013] Another object of the present invention is to provide a
method of tracking the motion of a patient's actual bone through
space and showing the same on a computer screen.
[0014] Yet another object of the present invention is to provide a
method of tracking at least two bones relative to one another as
three dimensional models on a computer screen as the actual bones
are taken through a range of motion.
[0015] It is also an object of the present invention to provide a
method of diagnosis for joint conditions based on a database of
kinematic or other information about joint motion.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] FIG. 1 is a posterior view of a human knee joint in a fixed
position;
[0017] FIG. 2 is a posterior view of a human knee joint in an
extended position;
[0018] FIG. 3 is an exemplary process flow diagram using exemplary
methods within the scope of the present invention;
[0019] FIG. 4 is a schematic diagram of the modules of an exemplary
diagnostic system;
[0020] FIG. 5 is a screen shot of a software user interface for
bone modeling;
[0021] FIG. 6 is an anterior view of the bones of a human knee
joint in an extended position;
[0022] FIG. 7 is a pictorial representation of a human leg having
an exemplary brace attached to a distal segment of the femur, and
exemplary brace attached to a proximal segment of the tibia, a
sensor mounted proximate the patella, and a foot pressure sensing
shoe;
[0023] FIG. 8 is a is an illustration of a CT slice of the
transcutaneous detection of a bone's surface using pulse echo
A-mode ultrasound;
[0024] FIG. 9 is a schematic of an exemplary inertia-based
localizer circuit;
[0025] FIG. 10 is a schematic of an exemplary brace circuit
architecture;
[0026] FIG. 11 is a circuit schematic of an exemplary high voltage
amplifier;
[0027] FIG. 12 is a circuit layout for the exemplary high voltage
amplifier of FIG. 11;
[0028] FIG. 13 is a block diagram for an exemplary high voltage
multiplexer;
[0029] FIG. 14 is a block diagram for an exemplary receiving
circuit;
[0030] FIG. 15 is a pictorial representation of an exemplary
kinematics tracking brace;
[0031] FIG. 16 is a pictorial representation of an alternative
exemplary kinematics tracking brace;
[0032] FIG. 17 is a pictorial representation of a further
alternative kinematics exemplary tracking brace;
[0033] FIG. 18 is a pictorial representation of a vibration
detection module;
[0034] FIGS. 19A, 19B, and 19C are pictorial representations of
exemplary kinematics data, vibration signal, and force data
respectively;
[0035] FIG. 20 is a graphical representation showing average ACLD
medial and lateral condyle contact positions during a deep knee
bend activity;
[0036] FIGS. 21A, 21B, and 21C are a series of views showing
contact path tracking in accordance with the exemplary
embodiments;
[0037] FIG. 22 is a is a schematic of the overall classification
system flow chart;
[0038] FIG. 23 is a schematic representation of an exemplary neural
network classifier;
[0039] FIG. 24 is an exemplary process flow for training an
exemplary neural network; and
[0040] FIG. 25 is an exemplary process flow for knee deficiency
diagnosis using a trained neural network.
DETAILED DESCRIPTION
[0041] The exemplary embodiments of the present invention are
described and illustrated below to encompass diagnosis of bodily
abnormalities and, more particularly, devices and methods for
evaluating the physiological condition of bodily tissue to discern
whether abnormalities exist and the next of any abnormalities. Of
course, it will be apparent to those of ordinary skill in the art
that the preferred embodiments discussed below are exemplary in
nature and may be reconfigured without departing from the scope and
spirit of the present invention. However, for clarity and
precision, the exemplary embodiments as discussed below may include
optional steps, methods and features that one of ordinary skill
should recognize as not being a requisite to fall within the scope
of the present invention. In exemplary fashion, the embodiments
disclosed herein are described with respect to diagnosing a knee
joint injury. Nevertheless, the embodiments may be utilized to
diagnose other joints and bodily tissue injuries, as the knee joint
is merely exemplary to facilitate an understanding of the
embodiments disclosed.
[0042] Referencing FIG. 4, a first exemplary diagnostic system
includes four modules: (1) a pulse echo A-mode ultrasound based 3D
model reconstruction (PEAUMR) module for constructing 3D patient
specific models of the knee joint bones; (2) a joint kinematics
tracking (JKT) module for tracking kinematics of the knee joint
using the patient-specific 3D model of the knee joint from the
PEAUMR module; (3) a vibration detection (VD) module for capturing
sounds emanating from the knee joint while in motion; and (4) an
intelligent diagnosis (ID) module for identifying pathological
cases of the knee joint using kinematic data and associated
vibration data gathered during the joint motion. Each of these four
modules is described in further detail in the following sections.
The foot sensor interacts in real time with these other modules
providing dynamic force data.
[0043] It will be understood by those of skill in the art that the
system described above is usable with or without the use of the
vibration detection module. For example, one may use the present
invention by mathematically describing the relative motion of bones
in a patient's joint as such motion is tracked in a 3D patient
specific bone model and comparing such description with a database
of mathematical descriptions of joint motion. The database could
contain mathematical descriptions of healthy or clinically
undesirable joint motion.
[0044] Referring to FIG. 5, the PEAUMR module constructs a 3D model
of a subject's (e.g., a patient) bones by transcutaneously
acquiring a set of 3D data points that in total are representative
of the shape of the bone's surface using a tracked pulse echo
A-mode ultrasound probe. The probe consists of a single ultrasound
transducer attached to a global localizer. The global localizer may
be optical, inertial, electromagnetic or ultra wide band radio
frequency. The probe is battery-powered and connected wirelessly to
a computer in order to record the set points and construct a unique
or patient-specific bone model using an atlas-based deformable
model technique.
[0045] The computer includes software that interprets data from the
tracked pulse echo A-mode ultrasound probe and is operative to
construct the 3D models of the patient's bones, which will look
very similar to the model shown in FIG. 6. The patient-specific
bone is reconstructed using the set of points collected from the
bone's surface transcuateously by the tracked ultrasound probe.
These points are then used by the atlas-based deformable model
software to reconstruct the 3D model of the patient's bone.
[0046] In exemplary form, the software includes a plurality of bone
models of the femur, tibia, and patella that are classified, for
example, based upon ethnicity, gender, skeletal bone to be modeled,
and the side of the body the bone is located. Each of these
classifications is accounted for by the dropdown menus of the
software so that the model initially chosen by the software most
closely approximates the bone of the patient.
[0047] After the software selects the bone model to approximate the
bone of the patient, the ultrasound transducer probe is
repositioned on the exterior of the skin and data points are
generated and applied to the model bone (in this case a distal
femur), numerically recorded and viewable in a data window, and
ultimately utilized by the software to conform the bone model to
the patient's actual bone shape. Obviously, a higher number of data
points imposed on the model will generally result in a more
accurate patient model. Nevertheless, in view of the model bones
already taking into account numerous traits of the patient
(ethnicity, gender, bone modeled, and body side of the bone), it is
quite possible to construct an accurate patient-specific 3D model
with as few as 150 data points, which typically can be taken by
repositioning the probe over the bone for 30 seconds for each bone.
In this example, it is preferable for the data to be acquired both
while the knee is bent and extended to more accurately shape the
ends of the bones. This same procedure is repeated for the
remaining bones of the joint, in this case the proximal end of the
tibia and the patella, in order for the software to combine the
bones thereby forming the joint. Ultrasound will not be affected
whether the patient has normal or prosthetic implant. The 3D model
of the femur can be resected and attached with the implanted CAD
model.
[0048] Referring to FIG. 7, the JKT module tracks the kinematics of
the knee joint using the patient-specific 3D bone models from the
PEAUMR module. In this exemplary embodiment, motion tracking of the
patient's knee joint bones is performed using one or more bone
motion tracking braces. In exemplary form, the bone motion tracking
brace includes pulse echo A-mode ultrasound transducers to
transcutaneously localize points on the bones surface.
Incidentally, the pulse echo A-mode ultrasound transducers may or
may not be identical to the pulse echo A-mode ultrasound
transducers used by the PEAUMR module. Commercially available
transducers for use with the exemplary embodiments include, without
limitation, the Olympus immersion unfocused 3.5 MHz transducer. The
force sensing shoe detects the ground reactive pressures
simultaneous with knee joint kinematic data acquisition.
[0049] Each ultrasound transducer is tracked using an accelerometer
or a sensor-specific localizer (or any other appropriate inertial
sensor). The resulting localized bone points generated from the
outputs of the ultrasound transducers are used in combination with
the patient specific 3D bone models to discern bone movement while
the knee joint is taken through a range of motion. In exemplary
form, three braces and a foot force sensing shoe are used to track
knee joint kinematics and dynamic forces: (a) a first brace is
positioned proximate the distal portion of the femur; (b) a second
brace is positioned proximate the distal end of the tibia; and, (c)
a third brace is positioned proximate the patella region.
[0050] Referring to FIG. 8, an exemplary bone motion tacking brace
includes a plurality of pulse echo A-mode ultrasound transducers
for transcutaneous detection of the bone's surface and
inertia-based localizers to track the motion of the ultrasound
transducers, which in turn, track the bones motion. Each brace is
wirelessly connected to a computer operative to perform
computations and visualization in real-time showing movements of
the patient-specific 3D bone models paralleling movements of the
patient's actual knee joint in a time synchronized manner. Each
exemplary brace include a rigid or semi-rigid body having a
plurality (two or more) of complementary metal oxide semiconductor
(CMOS) inertia-based sensors attached thereto. The position of each
sensor and/or transducer is tracked by using the equation of
motion: F.sub.r+F.sub.r*=0, where, F.sub.r is a summation of all
the generalized active forces in the system, and F.sub.r* is a
summation of all the generalized inertia forces in the system. The
homogenous transformation between the localizer's reference
coordinate frame and the world coordinate frame is calculated using
the positions of multiple inertia sensors. The following equation
calculates the linear movement of the transducer:
v(n+1)=v(n)+a(n)dt and s(n+1)=s(n)+v(n)dt-0.5a(n)dt2, where s(n+1)
is position at the current state, s(n) is the position from the
previous state, v(n+1) is instantaneous velocity of the current
state, v(n) is the velocity from previous state, and a(n) is the
acceleration from the accelerometer and dt is the sampling time
interval. The previous equations describe the dynamic motion and
positioning of a point in 3D Euclidean space. Additionally
information is needed to describe a 3D body orientation and motion.
The orientation of the transducer can be described by using a
gravity based accelerometer (example: ADXL-330, analog device) by
extracting the tilting information from each pair of orthogonal
axis. The acceleration output on x, y, or z due to gravity is equal
to the following: Ai=(Vout.sub.i-Voff)/S, where Ai is the
acceleration at x, y, or z axis, Vout.sub.i is the voltage output
from the x, y, or z axis, Voff is the offset voltage, and S is the
sensitivity of the accelerometer. The yaw, pitch and row can be
calculated as shown in the following:
.rho. = arc tan ( Ax A Y 2 + A Z 2 ) ##EQU00001## .PHI. = arc tan (
Ay A X 2 + A Z 2 ) ##EQU00001.2## .theta. = arc tan ( A Y 2 + A Z 2
Az ) ##EQU00001.3##
where pitch is .rho., which is x-axis relative to the ground, roll
is .phi., which is y-axis relative to the ground, and row is
.theta., which is z-axis relative to the ground. Since the
accelerometer is based using gravity, the orientation does not
require information from the previous state once the sensor is
calibrated. The static calibration requires the resultant sum of
accelerations from the 3 axis equal to 1 g. Alternatively, an
orientation sensor that gives us yaw, pitch, and row information of
the body are also commercially available (example: IDG-300,
Invensense). The orientation of the transducer can then be resolved
by using direction cosine matrix transformation:
[ X 2 Y 2 Z 2 ] = [ C .theta. C .PHI. C .theta. S .PHI. S .rho. - S
.theta. C .rho. C .theta. S .PHI. C .rho. - S .theta. S .rho. S
.theta. C .PHI. S .theta. S .PHI. S .rho. - C .theta. C .rho. S
.theta. S .PHI. C .rho. - C .theta. S .rho. - S .PHI. C .PHI. S
.rho. C .theta. C .rho. ] [ X 1 Y 1 Z 1 ] ##EQU00002##
where C shorts for cosine and S shorts for sine.
[0051] Referring to FIG. 9, an accelerometer based localizer is
used to track each pulse echo A-mode ultrasound transducer mounted
to the brace. The localizer comprises a plurality of nodes, with
each node comprising a CMOS accelerometer and a temperature sensor
for thermal drift comparison. Each node is integrated to minimize
noise and distortion. The outputs of the accelerometers regarding
the X, Y, and Z coordinates and temperature sensor are directed to
a multiplexer that multiplexes the signals. Multiplexed outputs are
amplified by an amplifier and then directed to an analog-to-digital
converter. The digital conversion of the signal can be performed
within or outside the CMOS sensors chip. Outputted digital signals
are directed to a wireless transmitter by way of a parallel
input/serial output device.
[0052] Referring to FIG. 10, each of the three exemplary design
alternatives for the brace has a similar electronic architecture.
An exemplary electronic architecture includes a high voltage
amplifier circuit feeding a voltage multiplexer circuit to excite
each ultrasound transducer and thereby acting as an analog switch.
The echo signals from each transducer are multiplexed pursuant to a
logic control directing the opening of the switches in the
multiplexer circuit at precise intervals. An exemplary logic
control is the MSP430 available from Texas Instruments. The output
from the multiplexer circuit is amplified by an amplifier circuit,
signal conditioned using a signal conditioning circuit, and
digitized using an analog-to-digital converter. Electric power to
the foregoing components is supplied by way of a battery, which
also supplies power to a wireless transmitter module. In exemplary
form, the wireless transmitter module utilizes the universal
asynchronous receiver/transmitter (UART) protocol. The module
includes a wireless transmitter circuit receiving the output of the
first in-first out (FIFO) buffer of the analog-to-digital converter
by way of a serial interface. An output from the wireless
transmitter circuit is conveyed using a serial link coupled to an
antenna. Signals conveyed through the antenna are broadcast for
reception by a wireless receiver coupled to a controller
computer.
[0053] Referring to FIGS. 11 and 12, an exemplary high voltage
circuit is utilized to trigger and generate the excitation energy
for the piezoelectric crystal in the transducer. Exemplary high
voltage circuits for use in this embodiment include, without
limitation, the pulsar integrated circuit (HV379) available from
Supertex.
[0054] Referencing FIG. 13, an exemplary high voltage multiplexer
is utilized to trigger and excite multiple piezoelectric
transducers without increasing the number of high voltage circuits
mentioned with regard to FIG. 11. Exemplary high voltage
multiplexers for use in this embodiment include, without
limitation, the high voltage multiplexer (HV2221) available from
Supertex. The advantage of using a high voltage multiplexer is the
ability to use CMOS level control circuitry, thereby making the
control logic compatible with virtually any microcontroller or
field programmable gate array commercially available.
[0055] Referring to FIG. 14, an exemplary receiving circuit, which
comprises the multiplexer circuit, the amplifier circuit, the
signal conditioning circuit, and the analog-to-digital converter,
is utilized to receive the echo signals from each transducer.
Exemplary receiving circuits for use in this embodiment include,
without limitation, the AD9271 8-channel ultrasound receiving
integrated circuit available from Analog Devices.
[0056] Referring to FIG. 15, a first exemplary bone tracking brace
includes a plurality of transducers mounted thereto. Each
transducer is responsible for determining the location of a point
on the surface of the bone for each motion tracking frame. Problems
of locating and tracking the bone using ultrasound data are reduced
as the motion of the bone relative to the skin is small compared to
the gross joint motion. There are at least three approaches
disclosed herein for tracking the motion of the ultrasound
transducers themselves. The first approach, commonly referred to
herein as the ITT (Individual transducer tracking) approach,
involves each transducer in the brace having an inertia-based
localizer to individually track each transducer. Using the ITT
approach, in exemplary form, the transducers are held together by
flexible length straps.
[0057] Referencing FIG. 16, a second approach, commonly referred to
herein as the ITML (Inter-transducers Mechanical Links) approach,
involves the transducers being connected to each other by movable
mechanical links. Each mechanical link includes length and angle
sensors that allow for detection of the movement of the transducers
relative to one another and the relative translational motions of
the links. Every two links are connected by a pivot pin that allows
rotation and translation of the links relative to each other. An
angle sensor is mounted to at least one link proximate the pivot
pin to allow for detection of the angle between the links. The ITML
approach features less localizers than the individual transducer
tracking design.
[0058] Referring to FIG. 17, a third approach, commonly referred to
herein as the RT (Rotating Transducer) approach, involves using a
single ultrasound transducer that is mounted to a carriage. The
carriage traverses along a track located on the inner circumference
of the brace. For example, the carriage may be moved along the tack
by a string loop that is wrapped around the drive shaft of a motor.
When the transducer reaches the motor, the rotation direction of
the motor is changed and the transducer moves in the opposite
direction.
[0059] An inertia-based localizer is mounted to the transducer to
track its motion. As the transducer rotates within the inner
circumference of the brace, it collects data as to the outer
circumferential topography of the bone surface. By using a single
transducer, the RT approach includes the advantage of lower cost
than the stationary transducer designs and higher accuracy due to
the greater number of localized bone surface points for each
tracking step, while maintaining a mechanical flexibility.
[0060] Referring to FIG. 18, a third module of the exemplary
diagnostic system, the vibration detection module, includes thin
film accelerometers that detect the vibration produced by motion of
the knee joint. Thin film accelerometers are used in lieu of sound
sensors, because of better performance and less noise
susceptibility. In exemplary form, the thin film accelerometers may
be the same ones used for the localizer, as well as having the same
circuitry for driving the accelerometers. The accelerometers are
attached to the patients and communicatively connected to the
kinematic tracking braces so the outputs from the accelerometers
can be amplified, digitized, and sent wirelessly to the controller
computer.
[0061] Referring to FIG. 22, X-ray video fluoroscopy and in-vivo
measurements of dynamic knee kinematics are important for
understanding the effects of joint injuries, diseases, and
evaluating the outcome of surgical procedures. In exemplary form,
using the two aforementioned techniques, six degrees of freedom
(DOF) are determined between the femur and tibia, femur and
patella, and tibia and patella that involve the position and
orientation of each with respect to the other. The accuracy of this
approach is within one degree of rotation and one mm of translation
(except for translation parallel to the viewing direction).
Although this approach is highly accurate, it constrains the
patient to remain within the small working volume of the
fluoroscope unit and subjects the patient to ionizing radiation for
a prolonged period of time. For most dynamic activities where the
joints are loaded such as running, jumping, or other dynamic
activities, fluoroscopy is an unacceptable alternative. To address
this deficiency in preexisting approaches, an exemplary system
accurately measures joint motion during dynamic activities using a
portable brace, such as those previously discussed herein. By using
a portable brace having sensors mounted thereto, X-ray fluoroscopy
may be omitted.
[0062] Implementation of joint movement visualization includes
using the exemplary 3C model reconstruction using pulse-echo A-mode
ultrasound system to measure vibrations produced to accurately
localize the exact vibration center and causes for its occurrence.
The interpretation of the vibration and kinematic data is a
complicated task involving an in-depth understanding of data
acquisition, training data sets and signal analysis, as well as the
mechanical system characteristics. Vibrations generated through the
implant components, bones, and/or soft tissues interaction result
from a forced vibration induced by driving force leading to a
dynamic response. The driving force can be associated with the
impact following knee ligament instability, bone properties, and
conditions. A normal, intact knee will have a distinct pattern of
motion, coupled with distinct vibrational characteristics. Once
degeneration or damage occurs to the knee joint, both the kinematic
patterns and vibrational characteristics become altered. This
altering, for each type of injury or degeneration, leads to
distinct changes that can be captured using both kinematic and
vibration determination.
[0063] Referencing FIG. 25, a fourth module of the exemplary
diagnostic system, the intelligent diagnosis module, which is a
software module, is operative to diagnose ligament, other soft
tissue, and bone injuries. From previous studies, normal and
anterior cruciate ligament deficient (ACLD) knee subjects exhibit a
similar pattern of posterior femoral translation during progressive
knee flexion, but the subjects exhibit different axial rotation
patterns of 30 degrees of knee flexion. Accordingly, the diagnostic
module is a two stage device that includes a first stage involving
motion measurement extraction, while a second stage classifies any
injury that is detected.
[0064] This first stage includes acquisition of kinematic feature
vectors using multiple physiological measurements taken from the
patient while the patient moves the joint in question through a
range of motion. Exemplary measurements include, without
limitation, medical condyle anteroposterior motion (MAP) and
lateral condyle anteroposterior (LAP), with the latter pertaining
to the anterior-posterior NP distance of the medial and lateral
condyle points relative to the tibia geometric center. Other
exemplary measurements include LSI (distance between the lateral
femoral condyle and the lateral tibial plateau) and MSI (distance
between the medial femoral condyle and the medial tibial plateau)
which are S/I (superior/inferior) distances of the lateral and
medial condyle points to the tibial plane. Further exemplary
measurements include condyle separation, which is the horizontal
(x-y plane) distance between the two minimum condyle points to the
tibia (See FIG. 21). Feature vectors also include the femoral
position with respect to the tibia which is defined by three Euler
angles and three translation components in addition to the
vibration signal, and force data (see FIGS. 19A, 19B, and 19C).
FIG. 20 is an exemplary graphical representation showing average
ACLD medial and lateral condyle contact positions during a deep
knee bend activity.
[0065] Referring to FIG. 22, the motion features vectors extracted
from the kinematic and vibration analyses are output to a
multilayer back propagation neural network for determining the
injured ligament.
[0066] Referencing FIG. 23, an exemplary neural network classifier
has multiple binary outputs. Each output is either a one or zero,
with one corresponding to yes and zero corresponding to no. In this
exemplary neural network classifier, each output represents the
response of the neural network to a particular injury type; for
example one output will represent the response for anterior
cruciate ligament deficiency (ACLD), its state will be one if an
ACL injury is detected, and zero otherwise. Obviously, the neural
network may be significantly more sophisticated or less
sophisticated, depending upon the underlying model of the joint in
question.
[0067] Referring to FIG. 24, construction of the exemplary neural
network (NN) includes formulating a supervised classifier using a
training set of the kinematic and vibration data corresponding to
normal and injured knee joints. The NN is trained with a set of
vectors. Each vector consists of data (kinematics, vibrations and
forces) collected from one joint. Fluoroscopy data can be used to
calculate the kinematics. Once the NN is trained, it can be used to
classify new cases and categorize the injury type using these
kinematics, vibration and forces data. Those skilled in the art
will readily understand that the types and classifications desired
to be accommodated by the neural network necessarily include
training the neural network on these very types and
classifications. Exemplary types and classifications of injuries to
a mammalian knee joint include, without limitation, osteoarthritic
conditions, soft tissue damage, and abnormal growths. Likewise, the
neural network also needs to be trained as to indicators of normal
knee function. In this manner, once the neural network is trained,
it has the capability to differentiate between and output diagnosis
data concerning normal and abnormal knee conditions.
[0068] Referencing FIG. 25, the vibration and kinematics features
of a person's knee joint are compiled and fed to the trained neural
network. The trained neural network then diagnoses the condition of
the patient's knee joint, identifying and degeneration by type and
severity.
[0069] Exemplary embodiments may be adapted to collect data outside
of a clinical setting. For example, an exemplary embodiment may be
worn by a patient for an extended period of time while performing
normal activities. For example, a patient may wear vibration
sensors and/or a kinematics tracking brace during activities that
are not reproducible in the office (for example, weight lifting,
racquet ball etc.) that elicit the pain or symptom. In some
embodiments, the patient may turn the device on immediately prior
to the activity and/or the patient may mark the event when it
occurs. This enables analysis of the data just a few seconds before
the marked time to see what abnormal sounds or joint kinematic were
occurring. Data may be stored on a portable hard drive (or any
other portable storage device) and then may be downloaded to
exemplary systems for analysis. The data can be transmitted and
stored in a computer wirelessly. It can also be stored with a
miniature memory drive if field data is desired. If the occurrence
of the pain was more random, exemplary devices allow continuous
gathering of data. In embodiments, the patient may mark the event.
Devices capable of continuous monitoring may require a larger data
storage capacity.
[0070] It is also understood that while the exemplary embodiments
have been described herein with respect to a knee joint, those
skilled in the art will readily understand that the aforementioned
embodiments may be easily adapted to other joints of a mammalian
animal. For example, embodiments may be adapted for use on hips,
ankles, toes, spines, shoulders, elbows, wrists, fingers, and
temporomandibular joints.
[0071] Following from the above description and invention
summaries, it should be apparent to those of ordinary skill in the
art that, while the methods and apparatuses herein described
constitute exemplary embodiments of the present invention, the
invention contained herein is not limited to this precise
embodiment and that changes may be made to such embodiments without
departing from the scope of the invention as defined by the claims.
Additionally, it is to be understood that the invention is defined
by the claims and it is not intended that any limitations or
elements describing the exemplary embodiments set forth herein are
to be incorporated into the interpretation of any claim element
unless such claim limitation is explicitly stated. Likewise, it is
to be understood that it is not necessary to meet any or all of the
identified advantages or objects of the invention disclosed herein
in order to fall within the scope of any claims. Since the
invention is defined by the claims and since inherent and/or
unforeseen advantages of the present invention may exist even
though they any not have been explicitly discussed herein.
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